The present invention relates to non-invasive functional assessment of coronary artery stenosis, and more particularly, to machine learning based non-invasive functional assessment of coronary artery stenosis from medical image data.
Cardiovascular disease (CVD) is the leading cause of deaths worldwide. Among various CVDs, coronary artery disease (CAD) accounts for nearly fifty percent of those deaths. Despite significant improvements in medical imaging and other diagnostic modalities, the increase in premature morbidity and mortality for CAD patients is still very high. The current clinical practice for diagnosis and management of coronary stenosis involves the assessment of the diseased vessel either visually or by Quantitative Coronary Angiography (QCA). Such assessment provides the clinician with an anatomical overview of the stenosis segment and parent vessel, including the area reduction, lesion length, and minimal lumen diameter, but does not provide a functional assessment of the effect of the lesion on blood flow through the vessel. Measuring the fractional flow reserve (FFR) by inserting a pressure wire into the stenosed vessel has been shown to be a better option for guiding revascularization decisions, since the FFR is more effective in identifying ischemia causing lesions, as compared to invasive angiography. QCA only evaluates the morphological significance if the stenosis and has a number of other limitations. Pressure wire based FFR measurements involve risks associated with the intervention necessary to insert the pressure wire into the vessel, and for a very narrow stenosis, the pressure wire may induce an additional pressure drop.
Recently, mechanistic models have been proposed that use mathematical equations to model the physics of the blood flow in a three-dimensional anatomical model of the coronary vessels of a patient extracted from medical images. Such approaches rely on physics-based mathematical equations to model the physiology at rest and at hyperemia, thereby allowing one to numerically solve the equations on a computer and determine the flow and pressure drop for an individual patient. The most widely used physics-based model is the Navier-Stokes equation, which is a non-linear partial differential equation that is based on principles of mass, momentum, and energy conservation and is used to characterize the flow of blood in the coronary arteries. This is often coupled with mathematical equations that model the physiology of the upstream (heart, aorta) and downstream (myocardium) regions of the anatomy. Depending on the complexity and clinical use case, these methods can be used to incorporate physiological models at various scales. Although various types of physics-based models, boundary conditions, and physiological assumptions have been proposed for blood flow, a common theme of mechanistic models is their use of mathematical equations to model the various physiological interactions explicitly. However, a drawback of such mechanistic models is the high computational cost and complexity of associated with the model preparation and numerical solution of the physics-based equations. Additionally, such mechanistic models typically incorporate only anatomical and some partial physiological measurements, and leave out other meaningful measurements.